MAT 495 - Fundamentals of Data Science

College of Arts and Sciences
3 credit(s) At least 1x fall or spring

Double Numbered with: MAT 695
Fundamental methods for data science, such as regression, linear discriminant analysis, k-nearest neighbors, support vector machine, k-means, principal component analysis, and nonlinear dimension reduction. Performance evaluation and model selection. Additional work required of graduate students.
PREREQ: (MAT 331 AND MAT 521) OR (MAT 503 AND MAT 523)